Model Registry Concepts

Model Registry Concepts

This lesson deepens MLOps & DevOps for AI Systems using the same subject areas emphasized by official documentation: MLflow and Kubeflow docs: experiment tracking, pipelines, model registry, serving, GPU infrastructure, drift monitoring and LLMOps. The goal is to turn Model Registry Concepts into a production skill: you should know the concept, the configuration surface, the safety controls, the operational checks, and the rollback path.

Documentation Coverage

• Core terms and object model for this topic.
• Configuration options, defaults, and lifecycle behavior from the docs.
• Security, reliability, and ownership boundaries.
• Validation steps before and after the change.
• Common failure modes and diagnostic signals.

Production Implementation Flow

1. Define the source of truth: Git, configuration, API, state file, or control plane.
2. Design the safest repeatable workflow, including dry-run or plan output where possible.
3. Attach CI/CD, policy, security, and peer-review gates.
4. Observe metrics, logs, events, or traces after the change.
5. Document rollback, escalation owner, and evidence for the change record.

make verify
make test
make security
make deploy-plan
make rollback-plan

Mastery Standard

You understand Model Registry Concepts when you can explain it, configure it, test it, monitor it, and recover it under incident pressure without relying on undocumented manual steps.